Researchers commonly utilize biological assays (bioassays) during drug discovery research to measure the pharmacological effects on biological matter of substances at increasing concentrations. The pharmacological effects of a substance at increasing concentrations may be quantified as dose-response values that correspond to respective dose concentrations. Data analysis of the dose-response data may include plotting the dose-response values against the dose concentrations and using regression (e.g., least squares regression) to identify a curve that “best” fits to the dose-response data (i.e., curve fitting). A parameterized curve fit function may correspond to the curve that “best” fits to the dose-response data, and regression analysis may provide estimates for the values of the parameters of the curve fit function. Regression analysis may also provide values for standard error and the confidence intervals of the curve fit function parameters.
In some circumstances, although the identified dose-response curve or curve fit function may be the “best” fit for the dose-response data, one or more parameters of the curve fit function may not be reliable, having relatively high values for the standard error and the confidence interval width. Unreliable parameters may indicate, for example, that a particular curve fit function is not a good choice for the dose-response data, or that there is an insufficient amount of dose-response data.
Researchers, however, may not appreciate the significance of high values for standard error and the confidence interval width of a parameter of the curve fit function. As a result, researchers may fail to recognize that one or more parameters of the curve fit function are unreliable.
Therefore, a need exists for indicating the reliability of the parameters of a curve fit function during data analysis.